Methods and applications of longitudinal data analysis

Methods and applications of longitudinal data analysis

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas o...

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Detalles Bibliográficos
Otros Autores: Liu, Xian author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Amsterdam, [Netherlands] : Academic Press 2016
Edición:1st edition
Materias:
Longitudinal method.
Multivariate analysis.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629690906719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Biography
  • Preface
  • Chapter 1 - Introduction
  • 1.1 - What is longitudinal data analysis?
  • 1.2 - History of longitudinal analysis and its progress
  • 1.3 - Longitudinal data structures
  • 1.3.1 - Multivariate data structure
  • 1.3.2 - Univariate data structure
  • 1.3.3 - Balanced and unbalanced longitudinal data
  • 1.4 - Missing data patterns and mechanisms
  • 1.5 - Sources of correlation in longitudinal processes
  • 1.6 - Time scale and the number of time points
  • 1.7 - Basic expressions of longitudinal modeling
  • 1.8 - Organization of the book and data used for illustrations
  • 1.8.1 - Randomized controlled clinical trial on the effectiveness of acupuncture treatment on PTSD
  • 1.8.2 - Asset and health dynamics among the oldest old (AHEAD)
  • Chapter 2 - Traditional methods of longitudinal data analysis
  • 2.1 - Descriptive approaches
  • 2.1.1 - Time plots of trends
  • 2.1.2 - Paired t-test
  • 2.1.3 - Effect size between two means and its confidence interval
  • 2.1.3.1 - General specification of effect size
  • 2.1.3.2 - Meta-analysis on an estimated effect size
  • 2.1.3.3 - Computation of confidence intervals for effect size from a single study
  • 2.1.4 - Empirical illustration: descriptive analysis on the effectiveness of acupuncture treatment in reduction of PTSD sym...
  • 2.2 - Repeated measures ANOVA
  • 2.2.1 - Specifications of one-factor ANOVA
  • 2.2.2 - One-factor repeated measures ANOVA
  • 2.2.3 - Specifications of two-factor repeated measures ANOVA
  • 2.2.4 - Empirical illustration: a two-factor repeated measures ANOVA - the effectiveness of acupuncture treatment on PCL re...
  • 2.3 - Repeated measures MANOVA
  • 2.3.1 - General MANOVA
  • 2.3.2 - Hypothesis testing on effects in MANOVA
  • 2.3.3 - Repeated measures MANOVA.
  • 2.3.4 - Empirical illustration: a two-factor repeated measures MANOVA on the effectiveness of acupuncture treatment on two ...
  • 2.4 - Summary
  • Chapter 3 - Linear mixed-effects models
  • 3.1 - Introduction of linear mixed models: three cases
  • 3.1.1 - Case I: one-factor linear mixed model with random intercept
  • 3.1.2 - Case II: linear mixed model with random intercept and random slope
  • 3.1.3 - Case III: linear mixed model with random effects and three covariates
  • 3.2 - Formalization of linear mixed models
  • 3.2.1 - General specification of linear mixed models
  • 3.2.2 - Variance-covariance matrix and intraindividual correlation
  • 3.2.3 - Formalization of variance-covariance components
  • 3.3 - Inference and estimation of fixed effects in linear mixed models
  • 3.3.1 - Maximum likelihood methods
  • 3.3.2 - Statistical inference and hypothesis testing on fixed effects
  • 3.3.3 - Missing data
  • 3.4 - Trend analysis
  • 3.4.1 - Polynomial time functions
  • 3.4.2 - Methods to reduce collinearity in polynomial time terms
  • 3.4.3 - Numeric checks on polynomial time functions
  • 3.5 - Empirical illustrations: application of two linear mixed models
  • 3.5.1 - Linear mixed model on effectiveness of acupuncture treatment on PCL score
  • 3.5.2 - Linear mixed model on marital status and disability severity in older Americans
  • 3.6 - Summary
  • Chapter 4 - Restricted maximum likelihood and inference of random effects in linear mixed models
  • 4.1 - Overview of Bayesian inference
  • 4.2 - Restricted maximum likelihood estimator
  • 4.2.1 - MLE bias in variance estimate in general linear models
  • 4.2.2 - Specification of REML in general linear models
  • 4.2.3 - REML estimator in linear mixed models
  • 4.2.4 - Justification of the restricted maximum likelihood method
  • 4.2.5 - Comparison between ML and REML estimators
  • 4.3 - Computational procedures.
  • 4.3.1 - Newton-Raphson algorithm
  • 4.3.2 - Expectation-maximization algorithm
  • 4.4 - Approximation of random effects in linear mixed models
  • 4.4.1 - Best linear unbiased prediction
  • 4.4.2 - Shrinkage and reliability
  • 4.5 - Hypothesis testing on variance component G
  • 4.6 - Empirical illustrations: linear mixed models with REML
  • 4.6.1 - Linear mixed model on effectiveness of acupuncture treatment on PCL score
  • 4.6.2 - Linear mixed model on marital status and disability among older Americans
  • 4.7 - Summary
  • Chapter 5 - Patterns of residual covariance structure
  • 5.1 - Residual covariance pattern models with equal spacing
  • 5.1.1 - Compound symmetry (CS)
  • 5.1.2 - Unstructured pattern (UN)
  • 5.1.3 - Autoregressive structures - AR(1) and ARH(1)
  • 5.1.4 - Toeplitz structures - TOEP and TOEPH
  • 5.2 - Residual covariance pattern models with unequal time intervals
  • 5.2.1 - Spatial power model - SP(POW)
  • 5.2.2 - Spatial exponential model - SP(EXP)
  • 5.2.3 - Spatial Gaussian model - SP(GAU)
  • 5.2.4 - Hybrid residual covariance model
  • 5.3 - Comparison of covariance structures
  • 5.4 - Scaling of time as a classification factor
  • 5.4.1 - Scaling approaches for classification factors
  • 5.4.2 - Coding schemes of time as a classification factor
  • 5.5 - Least squares means, local contrasts, and local tests
  • 5.5.1 - Least squares means
  • 5.5.2 - Local contrasts and local tests
  • 5.6 - Empirical illustrations: estimation of two linear regression models
  • 5.6.1 - Linear regression model on effectiveness of acupuncture treatment on PCL score
  • 5.6.2 - Linear regression model on marital status and disability severity among older Americans
  • 5.7 - Summary
  • Chapter 6 - Residual and influence diagnostics
  • 6.1 - Residual diagnostics
  • 6.1.1 - Types of residuals in linear regression models.
  • 6.1.2 - Semivariogram in random intercept linear models
  • 6.1.3 - Semivariogram in the linear random coefficient model
  • 6.2 - Influence diagnostics
  • 6.2.1 - Cook's D and related influence diagnostics
  • 6.2.2 - Leverage
  • 6.2.3 - DFFITS, MDFFITS, COVTRACE, and COVRATIO statistics
  • 6.2.4 - Likelihood displacement statistic approximation
  • 6.2.5 - LMAX statistic for identification of influential observations
  • 6.3 - Empirical illustrations on influence diagnostics
  • 6.3.1 - Influence checks on linear mixed model concerning effectiveness of acupuncture treatment on PCL score
  • 6.3.2 - Influence diagnostics on linear mixed model concerning marital status and disability severity among older Americans
  • 6.4 - Summary
  • Chapter 7 - Special topics on linear mixed models
  • 7.1 - Adjustment of baseline response in longitudinal data analysis
  • 7.1.1 - Adjustment of baseline score and the Lord's paradox
  • 7.1.2 - Adjustment of baseline score in longitudinal data analysis
  • 7.1.3 - Empirical illustrations on adjustment of baseline score
  • 7.2 - Misspecification of the assumed distribution of random effects
  • 7.2.1 - Heterogeneity linear mixed model
  • 7.2.2 - Nonnormal random effect distribution in linear mixed models
  • 7.2.3 - Best predicted random effects in different distributions
  • 7.2.4 - Empirical illustration: comparison between BLUP and least squares means
  • 7.3 - Pattern-mixture modeling
  • 7.3.1 - Classification of heterogeneous groups
  • 7.3.2 - Basic theory of pattern-mixture modeling
  • 7.3.3 - Pattern-mixture model
  • 7.3.4 - Empirical illustration of pattern-mixture modeling
  • 7.4 - Summary
  • Chapter 8 - Generalized linear mixed models on nonlinear longitudinal data
  • 8.1 - A brief overview of generalized linear models
  • 8.2 - Generalized linear mixed models and statistical inferences.
  • 8.2.1 - Basic specifications of generalized linear mixed models
  • 8.2.2 - Statistical inference and likelihood functions
  • 8.2.3 - Procedures of maximization and hypothesis testing on fixed effects
  • 8.2.4 - Hypothesis testing on variance components
  • 8.3 - Methods of estimating parameters in generalized linear mixed models
  • 8.3.1 - Penalized quasi-likelihood method
  • 8.3.2 - Marginal quasi-likelihood method
  • 8.3.3 - The Laplace method
  • 8.3.4 - Gaussian quadrature and adaptive Gaussian quadrature methods
  • 8.3.5 - Markov Chain Monte Carlo methods
  • 8.4 - Nonlinear predictions and retransformation of random components
  • 8.4.1 - Best linear unbiased prediction based on linearization
  • 8.4.2 - Empirical Bayes BLUP
  • 8.4.3 - Retransformation method
  • 8.5 - Some popular specific generalized linear mixed models
  • 8.5.1 - Mixed-effects logistic regression model
  • 8.5.2 - Mixed-effects ordered logistic model
  • 8.5.3 - Mixed-effects multinomial logit regression models
  • 8.5.4 - Mixed-effects Poisson regression models
  • 8.5.5 - Survival models
  • 8.6 - Summary
  • Chapter 9 - Generalized estimating equations (GEEs) models
  • 9.1 - Basic specifications and inferences of GEEs
  • 9.1.1 - Specifications of "naïve" model with independence hypothesis
  • 9.1.2 - Basic specifications of GEEs
  • 9.1.3 - Specifications of working correlation matrix
  • 9.1.4 - Quasi-likelihood information criteria for GEEs
  • 9.2 - Other GEE approaches
  • 9.2.1 - Prentice's GEE approach
  • 9.2.2 - Zhao and Prentice's GEE method (GEE2)
  • 9.2.3 - GEE models on odds ratios
  • 9.3 - Relationship between marginal and random-effects models
  • 9.3.1 - Comparison between the two approaches
  • 9.3.2 - Use of GEEs to fit a conditional model
  • 9.4 - Empirical illustration: effect of marital status on disability severity in older Americans
  • 9.5 - Summary.
  • Chapter 10 - Mixed-effects regression model for binary longitudinal data.

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